Showing papers on "Inertial measurement unit published in 2007"

A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation

Abstract: In this paper, we present an extended Kalman filter (EKF)-based algorithm for real-time vision-aided inertial navigation. The primary contribution of this work is the derivation of a measurement model that is able to express the geometric constraints that arise when a static feature is observed from multiple camera poses. This measurement model does not require including the 3D feature position in the state vector of the EKF and is optimal, up to linearization errors. The vision-aided inertial navigation algorithm we propose has computational complexity only linear in the number of features, and is capable of high-precision pose estimation in large-scale real-world environments. The performance of the algorithm is demonstrated in extensive experimental results, involving a camera/IMU system localizing within an urban area.

An introduction to inertial navigation

Abstract: Until recently the weight and size of inertial sensors has prohibited their use in domains such as human motion capture. Recent improvements in the performance of small and lightweight micromachined electromechanical systems (MEMS) inertial sensors have made the application of inertial techniques to such problems possible. This has resulted in an increased interest in the topic of inertial navigation, however current introductions to the subject fail to sufficiently describe the error characteristics of inertial systems. We introduce inertial navigation, focusing on strapdown systems based on MEMS devices. A combination of measurement and simulation is used to explore the error characteristics of such systems. For a simple inertial navigation system (INS) based on the Xsens Mtx inertial measurement unit (IMU), we show that the average error in position grows to over 150 m after 60 seconds of operation. The propagation of orientation errors caused by noise perturbing gyroscope signals is identified as the critical cause of such drift. By simulation we examine the significance of individual noise processes perturbing the gyroscope signals, identifying white noise as the process which contributes most to the overall drift of the system. Sensor fusion and domain specific constraints can be used to reduce drift in INSs. For an example INS we show that sensor fusion using magnetometers can reduce the average error in position obtained by the system after 60 seconds from over 150 m to around 5 m. We conclude that whilst MEMS IMU technology is rapidly improving, it is not yet possible to build a MEMS based INS which gives sub-meter position accuracy for more than one minute of operation.

Map-Based Precision Vehicle Localization in Urban Environments

Abstract: Many urban navigation applications (e.g., autonomous navigation, driver assistance systems) can benefit greatly from localization with centimeter accuracy. Yet such accuracy cannot be achieved reliably with GPS-based inertial guidance systems, specifically in urban settings. We propose a technique for high-accuracy localization of moving vehicles that utilizes maps of urban environments. Our approach integrates GPS, IMU, wheel odometry, and LIDAR data acquired by an instrumented vehicle, to generate high-resolution environment maps. Offline relaxation techniques similar to recent SLAM methods [2, 10, 13, 14, 21, 30] are employed to bring the map into alignment at intersections and other regions of self-overlap. By reducing the final map to the flat road surface, imprints of other vehicles are removed. The result is a 2-D surface image of ground reflectivity in the infrared spectrum with 5cm pixel resolution. To localize a moving vehicle relative to these maps, we present a particle filter method for correlating LIDAR measurements with this map. As we show by experimentation, the resulting relative accuracies exceed that of conventional GPS-IMU-odometry-based methods by more than an order of magnitude. Specifically, we show that our algorithm is effective in urban environments, achieving reliable real-time localization with accuracy in the 10centimeter range. Experimental results are provided for localization in GPS-denied environments, during bad weather, and in dense traffic. The proposed approach has been used successfully for steering a car through narrow, dynamic urban roads.

A new multi-position calibration method for MEMS inertial navigation systems

Abstract: The Global Positioning System (GPS) is a worldwide navigation system that requires a clear line of sight to the orbiting satellites For land vehicle navigation, a clear line of sight cannot be maintained all the time as the vehicle can travel through tunnels, under bridges, forest canopies or within urban canyons In such situations, the augmentation of GPS with other systems is necessary for continuous navigation Inertial sensors can determine the motion of a body with respect to an inertial frame of reference Traditionally, inertial systems are bulky, expensive and controlled by government regulations Micro-electro mechanical systems (MEMS) inertial sensors are compact, small, inexpensive and most importantly, not controlled by governmental agencies due to their large error characteristics Consequently, these sensors are the perfect candidate for integrated civilian navigation applications with GPS However, these sensors need to be calibrated to remove the major part of the deterministic sensor errors before they can be used to accurately and reliably bridge GPS signal gaps A new multi-position calibration method was designed for MEMS of high to medium quality The method does not require special aligned mounting and has been adapted to compensate for the primary sensor errors, including the important scale factor and non-orthogonality errors of the gyroscopes A turntable was used to provide a strong rotation rate signal as reference for the estimation of these errors Two different quality MEMS IMUs were tested in the study The calibration results were first compared directly to those from traditional calibration methods, eg six-position and rate test Then the calibrated parameters were applied in three datasets of GPS/INS field tests to evaluate their accuracy indirectly by comparing the position drifts during short-term GPS signal outages

Ambulatory Position and Orientation Tracking Fusing Magnetic and Inertial Sensing

Abstract: This paper presents the design and testing of a portable magnetic system combined with miniature inertial sensors for ambulatory 6 degrees of freedom ( DOF) human motion tracking. The magnetic system consists of three orthogonal coils, the source, fixed to the body and 3-D magnetic sensors, fixed to remote body segments, which measure the fields generated by the source. Based on the measured signals, a processor calculates the relative positions and orientations between source and sensor. Magnetic actuation requires a substantial amount of energy which limits the update rate with a set of batteries. Moreover, the magnetic field can easily be disturbed by ferromagnetic materials or other sources. Inertial sensors can be sampled at high rates, require only little energy and do not suffer from magnetic interferences. However, accelerometers and gyroscopes can only measure changes in position and orientation and suffer from integration drift. By combing measurements from both systems in a complementary Kalman filter structure, an optimal solution for position and orientation estimates is obtained. The magnetic system provides 6 DOF measurements at a relatively low update rate while the inertial sensors track the changes position and orientation in between the magnetic updates. The implemented system is tested against a lab-bound camera tracking system for several functional body movements. The accuracy was about 5 mm for position and 3 degrees for orientation measurements. Errors were higher during movements with high velocities due to relative movement between source and sensor within one cycle of magnetic actuation

Non-GPS navigation for security personnel and first responders

Abstract: This paper introduces a “Personal Dead-reckoning” (PDR) navigation system for walking persons. The system is useful for monitoring the position of emergency responders inside buildings, where GPS is unavailable. The PDR system uses a six-axes Inertial Measurement Unit attached to the user’s boot. The system’s strength lies in the use of a technique known as “Zero Velocity Update” (ZUPT) that virtually eliminates the ill-effects of drift in the accelerometers. It works very well with different gaits, as well as on stairs, slopes, and generally on 3-dimensional terrain. This paper explains the PDR and presents extensive experimental results, which illustrate the utility and practicality of the system.

Relative Pose Calibration Between Visual and Inertial Sensors

Abstract: This paper proposes an approach to calibrate off-the-shelf cameras and inertial sensors to have a useful integrated system to be used in static and dynamic situations. When both sensors are integrated in a system their relative pose needs to be determined. The rotation between the camera and the inertial sensor can be estimated, concurrently with camera calibration, by having both sensors observe the vertical direction in several poses. The camera relies on a vertical chequered planar target and the inertial sensor on gravity to obtain a vertical reference. Depending on the setup and system motion, the translation between the two sensors can also be important. Using a simple passive turntable and static images, the translation can be estimated. The system needs to be placed in several poses and adjusted to turn about the inertial sensor centre, so that the lever arm to the camera can be determined. Simulation and real data results are presented to show the validity and simple requirements of the proposed methods.

Calibration and data fusion solution for the miniature attitude and heading reference system

Abstract: Development, calibration and alignment of a miniature magnetic and inertial measurement unit, which is used as an attitude and heading reference system, are presented. Several guidelines were followed during the design process to make the magnetic and inertial measurement unit suitable for various kinds of applications, thus the system is designed both as small as possible but still modular, consisting of three inertial sensor units, a magnetic sensor unit and a control unit. Complete calibration and alignment procedure is described and an adaptive Kalman filter concept for fusing various sensors’ attitude and heading data is introduced and discussed. The characteristics of the magnetic and inertial measurement unit as an attitude and heading reference system are evaluated. The algorithm showed remarkable performance in the orientation determination as the average root mean square error was less than 1.2° over the entire appliable operating range.

Adaptive Step Length Estimation Algorithm Using Low-Cost MEMS Inertial Sensors

Abstract: In this paper we introduce a MEMS based pedestrian navigation system (PNS) which consists of the low cost MEMS inertial sensor. An adaptive step length estimation algorithm using the awareness of the walk or run status is presented. Future u-Health monitoring systems will be essential equipment for mobile users under the ubiquitous computing environment. It is well known that the cost of energy expenditure in human walk or run changes with the speed of movement. Also the accurate walking distance is an important factor in calculating energy expenditure in human daily life. In order to compute the walking distance precisely, the number of occurred steps has to be counted exactly and the step length should be exactly estimated as well. However the step length varies considerably with the movement's speed and status. Therefore, we recognize the movement status such as walk or run of a pedestrian using the small-sized MEMS inertial sensors. Based on the result, a step length is estimated adaptively. The developed method can be applied to PNS and health monitoring mobile system.

Personal Dead-reckoning System for GPS-denied Environments

Abstract: This paper introduces a positioning system for walking persons, called "Personal Dead-reckoning" (PDR) system. The PDR system does not require GPS, beacons, or landmarks. The system is therefore useful in GPS-denied environments, such as inside buildings, tunnels, or dense forests. Potential users of the system are military and security personnel as well as emergency responders. The PDR system uses a 6-DOF inertial measurement unit (IMU) attached to the user's boot. The IMU provides rate-of-rotation and acceleration measurements that are used in real-time to estimate the location of the user relative to a known starting point. In order to reduce the most significant errors of this IMU-based system-caused by the bias drift of the accelerometers-we implemented a technique known as "Zero Velocity Update" (ZUPT). With the ZUPT technique and related signal processing algorithms, typical errors of our system are about 2% of distance traveled for short walks. This typical PDR system error is largely independent of the gait or speed of the user. When walking continuously for several minutes, the error increases gradually beyond 2%. The PDR system works in both 2-dimensional (2-D) and 3-D environments, although errors in Z-direction are usually larger than 2% of distance traveled. Earlier versions of our system used an unpractically large IMU. In the most recent version we implemented a much smaller IMU. This paper discussed specific problems of this small IMU, our measures for eliminating these problems, and our first experimental results with the small IMU under different conditions.

Vision-aided inertial navigation for pin-point landing using observations of mapped landmarks

Abstract: In this paper we describe an extended Kalman filter algorithm for estimating the pose and velocity of a spacecraft during entry, descent, and landing. The proposed estimator combines measurements of rotational velocity and acceleration from an inertial measurement unit (IMU) with observations of a priori mapped landmarks, such as craters or other visual features, that exist on the surface of a planet. The tight coupling of inertial sensory information with visual cues results in accurate, robust state estimates available at a high bandwidth. The dimensions of the landing uncertainty ellipses achieved by the proposed algorithm are three orders of magnitude smaller than those possible when relying exclusively on IMU integration. Extensive experimental and simulation results are presented, which demonstrate the applicability of the algorithm on real-world data and analyze the dependence of its accuracy on several system design parameters. © 2007 Wiley Periodicals, Inc.

Integration of Vision and Inertial Sensors for 3D Arm Motion Tracking in Home-based Rehabilitation

Abstract: The integration of visual and inertial sensors for human motion tracking has attracted significant attention recently, due to its robust performance and wide potential application. This paper introduces a real-time hybrid solution to articulated 3D arm motion tracking for home-based rehabilitation by combining visual and inertial sensors. Data fusion is a key issue in this hybrid system and two different data fusion methods are proposed. The first is a deterministic method based on arm structure and geometry information, which is suitable for simple rehabilitation motions. The second is a probabilistic method based on an Extended Kalman Filter (EKF) in which data from two sensors is fused in a predict-correct manner in order to deal with sensor noise and model inaccuracy. Experimental results are presented and compared with commercial marker-based systems, CODA and Qualysis. They show good performance for the proposed solution.

Inertial Aiding of Inverse Depth SLAM using a Monocular Camera

Abstract: This paper presents the benefits of using a low cost inertial measurement unit to aid in an implementation of inverse depth initialized SLAM using a hand-held monocular camera. Results are presented with and without inertial observations for different assumed initial ranges to features on the same dataset. When using only the camera, the scale of the scene is not observable. As expected, the scale of the map depends on the prior used to initialize the depth of the features and may drift when exploring new terrain, precluding loop closure. The results show that the inertial observations help to improve the estimated trajectory of the camera leading to a better estimation of the map scale and a more accurate localization of features.

Laboratory Experimentation of Autonomous Spacecraft Approach and Docking to a Collaborative Target

Abstract: A new laboratory test bed i s introduced, which enables the hardware -in -the -loop simulation of the autonomous approach and docking of a chaser spacecraft to a target spacecraft of similar mass. The test bed consists of a chaser spacecraft and a target spacecraft simulator s floating via air pads on a flat floor. The prototype docking interface mechanism of Defense Advanced Research Projec ts Agency’s (DARPA’s) Orbital Express mission is integrated on the spacecraft simulators. Relative navigation of the chaser spacecraft is obtained by fusing the measurements from a single -camera vision sensor and an inertial measurement unit , through Kalma n filters . The target is collaborative in the sense that a pattern of three infrared Light Emitting Diodes is mounted on it as reference for the relative navigation. Eight cold -gas on -off thrusters are used for the t ranslation of the chaser vehicle . They a re commanded using a non -linear control algorithm based on S ch mit t triggers. Furthermore, a reaction wheel is used for the vehicle rotation with a proportional derivative linear control. Experimental results are presented of both autonomous proximity maneu ver a nd autonomous docking of the chaser simulator to the non -floating target . The presented results validate the proposed estimation and control methods and demonstrate the capability of the test bed.

Simulation of an Inertial Acoustic Navigation System With Range Aiding for an Autonomous Underwater Vehicle

Abstract: This paper presents an integrated navigation system for underwater vehicles to improve the performance of a conventional inertial acoustic navigation system by introducing range measurement. The integrated navigation system is based on a strapdown inertial navigation system (SDINS) accompanying range sensor, Doppler velocity log (DVL), magnetic compass, and depth sensor. Two measurement models of the range sensor are derived and augmented to the inertial acoustic navigation system, respectively. A multirate extended Kalman filter (EKF) is adopted to propagate the error covariance with the inertial sensors, where the filter updates the measurement errors and the error covariance and corrects the system states when the external measurements are available. This paper demonstrates the improvement on the robustness and convergence of the integrated navigation system with range aiding (RA). This paper used experimental data obtained from a rotating arm test with a fish model to simulate the navigational performance. Strong points of the navigation system are the elimination of initial position errors and the robustness on the dropout of acoustic signals. The convergence speed and conditions of the initial error removal are examined with Monte Carlo simulation. In addition, numerical simulations are conducted with the six-degrees-of-freedom (6-DOF) equations of motion of an autonomous underwater vehicle (AUV) in a boustrophedon survey mode to illustrate the effectiveness of the integrated navigation system.

Visual Odometry System Using Multiple Stereo Cameras and Inertial Measurement Unit

Abstract: Over the past decade, tremendous amount of research activity has focused around the problem of localization in GPS denied environments. Challenges with localization are highlighted in human wearable systems where the operator can freely move through both indoors and outdoors. In this paper, we present a robust method that addresses these challenges using a human wearable system with two pairs of backward and forward looking stereo cameras together with an inertial measurement unit (IMU). This algorithm can run in real-time with 15 Hz update rate on a dual-core 2 GHz laptop PC and it is designed to be a highly accurate local (relative) pose estimation mechanism acting as the front-end to a simultaneous localization and mapping (SLAM) type method capable of global corrections through landmark matching. Extensive tests of our prototype system so far, reveal that without any global landmark matching, we achieve between 0.5% and 1% accuracy in localizing a person over a 500 meter travel indoors and outdoors. To our knowledge, such performance results with a real time system have not been reported before.

IMU-based localization and slip estimation for skid-steered mobile robots

Abstract: Localization and wheel slip estimation of a skid- steered mobile robot is challenging because of the complex wheel/ground interactions and kinematics constraints. In this paper, we present a localization and slip estimation scheme for a skid-steered mobile robot using low-cost inertial measurement units (IMU). We first analyze the kinematics of the skid-steered mobile robot and present a nonlinear Kalman filter (KF)- based simultaneous localization and slip estimation scheme. The KF-based localization design incorporates the wheel slip estimation and utilizes robot velocity constraints and estimates to overcome the large drift resulting from the integration of the IMU acceleration measurements. The estimation methodology is tested and validated experimentally with a computer vision- based localization system.

Apparatus and methods for using inertial sensing to navigate a medical device

Abstract: A system for remotely navigating a medical device in an operating region in a subject An inertial sensing device in the medical device distal end has one or more inertial sensors that provide information for locating the medical device A controller is operable to control movement of the medical device based on the locating information to navigate the device distal end to a target point

An Accurate Land-Vehicle MEMS IMU/GPS Navigation System Using 3D Auxiliary Velocity Updates

Abstract: In the last decade, the Land-Vehicle Navigation (LVN) market has grown rapidly. For most LVN systems, GPS is used for positioning. However, GPS has poor accuracy in urban areas due to signal blockages. Therefore, the LVN market has targeted the integration of other sensors with GPS. In this case, sensors' cost and size are major issues. Recent advances in MEMS inertial sensors made it possible to develop low-cost and compact IMUs. However, MEMS provide poor accuracy when used without updates (e.g., during GPS outages). In such periods, other updates are required for better performance. Vehicle full-stops, i.e., Zero-Velocity-Updates (ZUPTs), are usually applied for this purpose. However, this is not practical especially when GPS blockages are frequent. In this paper, 3D Auxiliary Velocity Updates (AVUs) are used, namely, non-holonomic constraints and odometer-derived velocity. Using kinematic MEMS/GPS data with several GPS signal blockages, the results showed a significant accuracy improvement after applying AVUs.

Sensor Integration for Satellite-Based Vehicular Navigation Using Neural Networks

Abstract: Land vehicles rely mainly on global positioning system (GPS) to provide their position with consistent accuracy. However, GPS receivers may encounter frequent GPS outages within urban areas where satellite signals are blocked. In order to overcome this problem, GPS is usually combined with inertial sensors mounted inside the vehicle to obtain a reliable navigation solution, especially during GPS outages. This letter proposes a data fusion technique based on radial basis function neural network (RBFNN) that integrates GPS with inertial sensors in real time. A field test data was used to examine the performance of the proposed data fusion module and the results discuss the merits and the limitations of the proposed technique

Invariant observers for attitude and heading estimation from low-cost inertial and magnetic sensors

Abstract: In this paper we propose invariant nonlinear observers for attitude and heading estimation using directly the measurements from low-cost inertial and magnetic sensors. In particular we propose a simple and easy-to-tune observer where moreover the estimated attitude behaves well even in the presence of magnetic disturbances.

Tight Coupling of Laser Scanner and Inertial Measurements for a Fully Autonomous Relative Navigation Solution

Abstract: The paper describes a fully autonomous relative navigation solution for urban environments (indoor and outdoor). The navigation solution is derived by combining measurements from a two-dimensional (2D) laser scanner with measurements from inertial sensors. This derivation relies on the availability of structures (lines and surfaces) within the scan range (80 m depth, typically). Navigation herein is performed in completely unknown environments. No map information is assumed to be available a priori. Indoor and outdoor live data test results are used to demonstrate performance characteristics of the laser/inertial integrated navigation. Relative positioning at the cm-level is demonstrated for indoor scenarios where well-defined features and good feature geometry are available. Test data from challenging urban environments show position errors at the meter-level after approximately 200 m of travel (between 0.6% and 0.8% of distance traveled).

Non-GPS navigation with the personal dead-reckoning system

Abstract: This paper introduces a positioning system for walking persons, called "Personal Dead-reckoning" (PDR) system. The PDR system does not require GPS, beacons, or landmarks. The system is therefore useful in GPS-denied environments, such as inside buildings, tunnels, or dense forests. Potential users of the system are military and security personnel as well as emergency responders. The PDR system uses a small 6-DOF inertial measurement unit (IMU) attached to the user's boot. The IMU provides rate-of-rotation and acceleration measurements that are used in real-time to estimate the location of the user relative to a known starting point. In order to reduce the most significant errors of this IMU-based system−caused by the bias drift of the accelerometers−we implemented a technique known as "Zero Velocity Update" (ZUPT). With the ZUPT technique and related signal processing algorithms, typical errors of our system are about 2% of distance traveled. This typical PDR system error is largely independent of the gait or speed of the user. When walking continuously for several minutes, the error increases gradually beyond 2%. The PDR system works in both 2-dimensional (2-D) and 3-D environments, although errors in Z-direction are usually larger than 2% of distance traveled. Earlier versions of our system used an impractically large IMU. In the most recent version we implemented a much smaller IMU. This paper discussed specific problems of this small IMU, our measures for eliminating these problems, and our first experimental results with the small IMU under different conditions.

Adaptive Fuzzy Prediction of Low-Cost Inertial-Based Positioning Errors

Abstract: Kalman filter (KF) is the most commonly used estimation technique for integrating signals from short-term high performance systems, like inertial navigation systems (INSs), with reference systems exhibiting long-term stability, like the global positioning system (GPS). However, KF only works well under appropriately predefined linear dynamic error models and input data that fit this model. The latter condition is rather difficult to be fulfilled by a low-cost inertial measurement unit (IMU) utilizing microelectromechanical system (MEMS) sensors due to the significance of their long- and short-term errors that are mixed with the motion dynamics. As a result, if the reference GPS signals are absent or the Kalman filter is working for a long time in prediction mode, the corresponding state estimate will quickly drift with time causing a dramatic degradation in the overall accuracy of the integrated system. An auxiliary fuzzy-based model for predicting the KF positioning error states during GPS signal outages is presented in this paper. The initial parameters of this model is developed through an offline fuzzy orthogonal-least-squares (OLS) training while the adaptive neuro-fuzzy inference system (ANFIS) is implemented for online adaptation of these initial parameters. Performance of the proposed model has been experimentally verified using low-cost inertial data collected in a land vehicle navigation test and by simulating a number of GPS signal outages. The test results indicate that the proposed fuzzy-based model can efficiently provide corrections to the standalone IMU predicted navigation states particularly position.

Design and Implementation of a Mechatronic, All-Accelerometer Inertial Measurement Unit

Abstract: This paper discusses the design, calibration, simulation, and experimental validation of a kinematically redundant inertial measurement unit that is based solely on accelerometers. The sensor unit comprises 12 accelerometers, two on each face of a cube. The location and direction of the sensors are determined so as to locally optimize the numerical conditioning of the system of governing kinematic equations. The orientational installation error of each sensor is identified by off-line iterative processing of the gravitational acceleration measurements made at a number of known orientations of the unit, thus allowing subsequent calibration. Furthermore, a novel procedure is developed through which the acceleration measurements can be used to directly determine the body angular velocity; this results in a major accuracy improvement over similar works whereby the angular velocity is obtained via integrating the angular acceleration. Experimental results are presented to validate the methodology, design, and implementation.